LCW-YOLO: An Explainable Computer Vision Model for Small Object Detection in Drone Images

Small targets in drone imagery are often difficult to accurately locate and identify due to scale imbalance and limitations, such as pixel representation and dynamic environmental interference, and the balance between detection accuracy and resource consumption of the model also poses challenges. Therefore, we propose an interpretable computer vision framework based on YOLOv12m, called LCW-YOLO. First, we adopt multi-scale heterogeneous convolutional kernels to improve the lightweight channel-level and spatial attention combined context (LA2C2f) structure, enhancing spatial perception capabilities while reducing model computational load. Second, to enhance feature fusion capabilities, we propose the Convolutional Attention Integration Module (CAIM), enabling the fusion of original features across channels, spatial dimensions, and layers, thereby strengthening contextual attention. Finally, the model incorporates Wise-IoU (WIoU) v3, which dynamically allocates loss weights for detected objects. This allows the model to adjust its focus on samples of average quality during training based on object difficulty, thereby improving the model’s generalization capabilities. According to experimental results, LCW-YOLO eliminates 0.4 M parameters and improves mAP@0.5 by 3.3% on the VisDrone2019 dataset when compared to YOLOv12m. And the model improves mAP@0.5 by 1.9% on the UAVVaste dataset. In the task of identifying small objects with drones, LCW-YOLO, as an explainable AI (XAI) model, provides visual detection results and effectively balances accuracy, lightweight design, and generalization capabilities.